Systems and methods for monitoring and analyzing cardiovascular states

ABSTRACT

A system and method for hemodynamic dysfunction detection may include at least one sensor configured to received one or more signals from a patient, a computing device in data communication with the at least one sensor, a computer-readable storage medium in communication with the computing device, an input device, and an output device. The system may include computer readable instructions to cause the system to receive at least one signal in the time domain from the sensor, determine at least one metric in the frequency domain from the at least one signal in the time domain, and determine the cardiovascular state of the patient from a combination of the at least one metric in the frequency domain and information contained in at least one database of cardiovascular states. The system may also notify a user of a immanent patient cardiovascular event and recommend one or more interventions to mitigate it.

CLAIM OF PRIORITY

This application claims priority to and benefit of U.S. ProvisionalApplication Ser. No. 61/865,114 filed Aug. 12, 2013 entitled “TemporalPattern-Based Hemodynamic Dysfunctional Detection and InterventionGuidance,” the disclosure of which is incorporated herein by referencein its entirety.

BACKGROUND

The primary role of the cardiovascular system is to facilitate adequatecirculating blood volume to provide sufficient oxygen delivery, therebymeeting the metabolic demands of the tissues and cells. The adequacy ofcirculating blood volume may be affected by the functional effectivenessof the cardiovascular system. A healthy cardiovascular system may becharacterized, in part, by its ability to maintain adequate oxygenatedblood flow and pressure in response to changes in the demand foroxygenated blood. Such changes may result from traumatic stresses orchanges in the metabolic health of tissues or organs reflected in theirability to extract or use oxygenated blood. An impaired cardiovascularsystem may not be able to supply sufficient oxygen to tissues or adaptto circulatory stress. If oxygen delivery to tissue has beencompromised, tissue hypoxia may occur. If tissue hypoxia is prolonged,acute cellular or organ damage may occur resulting in long term patientmorbidity or mortality.

Cardiovascular impairment can occur due to sudden pathology or trauma,resulting in shock. Alternatively, such impairment may occur in restingpatients due to underlying chronic pathologies, such as heart failure.Additionally, external volemic stressors, including some medicalprocedures, may cause fluid to transfer into or out of the arterialtree. As one example, ultrafiltration used for kidney replacementtherapy may result in an induced hypovolemic condition due to a mismatchbetween the rate of volume removal from the vasculature and the rate ofrefill of fluid volume from outside of the vasculature. In anotherexample, poor management of aquapheresis therapy for heart failurepatients having cardiac pulmonary edema may also result in a significantchange in patient fluid volume. Thus, a hypovolemic condition may beinduced if too much fluid is removed, or residual edema may result iftoo little is removed. For patients undergoing surgery, the vasodilationeffects of analgesics and paralytics may result in too little effectiveor maintained fluid volume. Alternatively, surgical patients may receiveexcess fluid volume from intravascular administration of normal salinesolution.

While hemodynamic dysfunction conditions may be present in an acute caresetting, the initial onset of such conditions may begin in other venueswith or without the patient presenting any related symptoms. Somenon-limiting examples of such non-acute care settings may includeclinics, physician offices, nursing homes, pre-hospital emergent caretransport facilities, transitional care facilities, and the home. It maybe understood that accurate detection of pre-symptomatic or earlysymptomatic hemodynamic dysfunction by caregivers in these non-acutesettings may permit the caregivers to intervene proactively, therebyavoiding a possible acute event or a least minimizing the adverseeffects on the patient. Typically, only non-invasive medical devicetechnologies are tolerated in such non-acute care settings.Additionally, caregivers at these facilities may not have sufficient ordetailed medical training to recognize when a patient has ahemodynamically unstable condition, or to accurately diagnose the typeof dysfunction and provide the necessary care.

SUMMARY

Before the present methods, systems and materials are described, it isto be understood that this disclosure is not limited to the particularmethodologies, systems, and materials described, as these may vary. Itis also to be understood that the terminology used in the description isfor the purpose of describing the particular versions or embodimentsonly, and is not intended to limit the scope.

In an embodiment, a method for determining a cardiovascular state of apatient in a stress condition may include receiving, by a computingdevice, at least one signal in the time domain from at least one sensorin operative communication with the patient in a stress condition, inwhich the at least one signal in the time domain is a pulse wavemeasurement of the patient, determining, by the computing device, atleast one metric in the frequency domain from the at least one signal inthe time domain, and determining, by the computing device, thecardiovascular state of the patient from the at least one metric in thefrequency domain and information from at least one database ofcardiovascular states.

In an embodiment, a system for determining a cardiovascular state of apatient in a stress condition may include at least one sensor configuredto received one or more signals from the patient, a computing device indata communication with the at least one sensor, a non-transitory,computer-readable storage medium in operable communication with thecomputing device, an input device in operable communication with thecomputing device, and an output device in operable communication withthe computing device. Further, the computer-readable storage medium maycontain one or more programming instructions that, when executed, causethe computing device to receive at least one signal in the time domainfrom the at least one sensor, in which the at least one signal in thetime domain is a pulse wave measurement of the patient, determine atleast one metric in the frequency domain from the at least one signal inthe time domain, and determine the cardiovascular state of the patientfrom the at least one metric in the frequency domain and informationfrom at least one database of cardiovascular states.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an illustrative model-based patient monitoring system inaccordance with some embodiments.

FIG. 2 depicts a computing device that may be used with the patientmonitoring system in accordance with some embodiments.

FIGS. 3A and 3B depict a pulse wave signal in the time domain and afrequency domain signal derived therefrom, respectively, in accordancewith some embodiments.

FIG. 4 depicts metrics associated with a pulse wave signal in the timedomain in accordance with some embodiments.

FIGS. 5A and 5B depict dispersion graphs of inter-peak occurrence timesof a pulse wave signal in accordance with some embodiments.

FIGS. 6A and 6B depict ECG traces in accordance with some embodiments.

FIG. 6C depicts an ECG trace and a pulse waveform trace in accordancewith some embodiments.

FIG. 7 depicts, in tabulated form, information in a database ofcardiovascular states in accordance with some embodiments.

FIG. 8 is a flow chart of an illustrative method of determining acardiovascular state of a patient in accordance with some embodiments.

DETAILED DESCRIPTION

A physiology monitoring system that employs one or more non-invasivemeasures to continuously assess a patient's physiology may include acomputing system along with one or more patient sensors to derivemetrics that, in conjunction with a database, can be used to recognizecardiovascular dysfunctional states.

A cardiovascular dysfunctional state can be in the form of inadequateglobal circulatory blood flow, inadequate circulatory blood volume, lowtissue perfusion, local ischemia, or inappropriate tissue or cellularoxygen extraction or use due to metabolic dysfunction. In someinstances, patients having multiple comorbidities can exhibit atypicalphysiological responses thereby causing hemodynamic dysfunctioncondition. For such patients, the patient monitoring system may furtherinclude strategies to enable detection of such atypical physiologicalresponses to hemodynamic dysfunctions. For example, a measurement ofsystolic blood pressure less than 90 mm Hg, indicating hypotension, byitself may be an inadequate measure of cardiovascular status for apatient diagnosed with advanced cardiac disease. Additional data, suchas the types and frequencies of occurrence of dysrhythmias—includingbradycardia, tachycardia, and abnormal R-R dispersion—may improve thecharacterization of the patient's cardiovascular state, thereby leadingto more effective treatment protocols.

As disclosed above, pre-symptomatic hemodynamic dysfunction may occuroutside of critical care facilities. Such alternative care venues maylack sophisticated technology required to diagnose the specificdysfunction and provide appropriate care. In some instances, suchalternative care venues may only have access to non-invasive patientmonitoring equipment capable of providing only traditional vital signs.Such monitoring equipment may have poor sensitivity for detecting earlystages of cardiovascular dysfunction as well as poor specificityregarding the underlying cardiovascular components that may contributeto this condition. Additionally, the personnel at such facilities maylack the specialized medical expertise needed to properly interpret thedata from the monitoring equipment. It may be appreciated that suchhealth care facilities and personnel may benefit from a physiologymonitoring system adapted to receive data from such non-invasive sensorsand provide an expert-like diagnosis of patient status that may bebeyond the expertise of the local health care personnel.

An exemplary physiological monitoring system capable of providingexpert-like diagnostic information regarding a patient status isdepicted in FIG. 1. Such a system may generally include a computingdevice 110 having one or more interfaces to receive time domainphysiological sensing signals from each of one or more physiologicalsensors 105 associated with a patient 100. The monitoring system mayalso contain, or be in communication with, one or more databases 115,135 containing information related to one or more patient conditions orstates. The computing device 110 may transform the one or more timesensing signals into one or more frequency domain metrics that may becompared to the information contained in the one or more databases 115,135. Based on the comparison, the computing device 110 may provide oneor more outputs 120, 125, 130 to be received by the patient or one ormore health care providers (hereafter, “user”).

In some non-limiting applications, the computing device 110 may simplymonitor the one or more physiological signals from the patient 100 andprovide the user with an output 120 including updated informationregarding the one or more time domain sensing signals, one or morederived time domain metrics, or one or more derived frequency domainmetrics. It may be appreciated that additional information related tothe patient condition and environment may also be displayed including,without limitation, patient blood pressure, patient temperature, andcurrent date and time. Such monitoring capabilities may be also be usedfor assessing the cardiovascular health of a patient in a normativestate, such as an elderly patient having an age-related decrease invascular reserve but who is otherwise healthy. Cardiovascular monitoringmay also be useful as part of a sports-training program to determine theeffectiveness of an athlete's training program.

In another non-limiting application, the computing device 110 maymonitor the one or more time domain sensing signals from the patient100, provide one or more derived time domain metrics or frequency domainmetrics therefrom, and provide the user with an output 125 including oneor more warning indicators of an emergent patient condition, such asbeing pre-symptomatic for a hemodynamic dysfunction condition. Suchwarning indicators may be based on one or more of the one or more timedomain sensing signals, one or more derived time domain metrics, one ormore related frequency domain metrics, and information obtained from adatabase of cardiovascular states 115. Warning indicators fornotification of a user may include, without limitation, one or more ofan audible alarm, a visual indicium on a computing device display, and atext message to a mobile communication device.

In still another non-limiting application, the computing device 110 maymonitor the one or more time domain sensing signals from the patient100, derive one or more time domain metrics and/or frequency domainmetrics therefrom, and provide the user with an output 130 related toone or more proposed standardized therapeutic protocols appropriate tothe patient's 100 cardiovascular status. Such therapeuticrecommendations may be based on the one or more time domain sensingsignals, one or more time domain and/or frequency domain metrics derivedfrom the time domain signals, information obtained from a database ofcardiovascular states 115, and additional information from a database oftherapeutic protocols 135.

In yet another alternative application, the computing device 110 maymonitor the one or more time domain sensing signals from the patient100, derive one or more time domain metrics and/or frequency domainmetrics therefrom, and provide the user with an output 130 related toone or more changes to a standardized therapeutic protocol based on oneor more of the one or more time domain sensing signals, one or morederived time domain metrics, one or more derived frequency domainmetrics, information obtained from a database of cardiovascular states115, information from a database of therapeutic procedures 135, thepatient's 100 medical history information 140, and additional patientmedical status information. Non-limiting examples of such patientmedical status may include one or more of an indicator of the patient'sage, an indicator of the patient's body mass, an indicator of thepatient's gender, an indicator of one or more patient co-morbidities, anindicator of one or more patient medications, an indicator of the dosageof each of the one or more patient medications, an indicator of one ormore patient therapies, an indicator of one or more patient surgeries,and an indicator of one or more patient genetic predispositions to oneor more pathologies.

In some non-limiting examples, the one or more sensors 105 may include atransmittance photo-optic sensor, a reflective photo-optic sensor, apressure transducer, a tonometry device, a strain gauge, an ultrasounddevice, an electrical impedance measurement device, and a radar device.Additional sensors 105 may include a blood pressure measurement device,a plethysmograph, and an electro-cardiograph device (ECG). It may beunderstood that the one or more sensors 105 may be in physical contactwith a surface of the patient or disposed within a natural cavity of thepatient, such as the mouth, ear canal, rectum, or vagina. Alternativesensors 105 may be remotely placed with respect to the patient's bodyand lack physical contact with the patient.

Examples of time domain signals that may be obtained by the computingdevice 110 from such sensors 105 may include a pulse volume waveform, apulse pressure waveform, a measurement of red blood density, anindicator of circulatory blood flow, a measure of arterial bloodpressure, a measurement of cardiac electrophysiology, a measurement ofvascular compliance, a measurement of specific muscle tissueoxygenation, and time-dependent changes in total fluid volume. Such timedomain signals may be representative of one or more of a patient'scardiac function, a patient's respiration function, and the complianceof the patient's vasculature. The one or more time domain signals may bereceived by the computing device from the patient experiencing one ormore of an injury, a pathological process, a surgical procedure, adiagnostic procedure, a therapeutic procedure, and a result of a geneticpre-disposition. Non-limiting examples of pathological processes mayinclude one or more of cardiac myopathy, chronic obstructive pulmonarydisease, chronic venous insufficiency, and renal failure. Non-limitingexamples of a surgical procedure may include any surgical procedurerequiring the use of an anesthesia agent, such as one or more of acardiac surgery, a vascular surgery, a pulmonary surgery, a renalsurgery, an abdominal surgery, and a cranial surgery. Non-limitingexamples of a therapeutic procedure may include any therapeuticprocedure to treat one or more components of the cardiovascular system,such as a dialysis procedure, a cardiovascular rehabilitation procedure,a skeletal muscle rehabilitation procedure, and an aquapheresisprocedure. Non-limiting examples of a diagnostic procedure may includeone or more cardiovascular reflex tests, including a cardiac functionassessment procedure, a vascular non-compliance assessment procedure, atissue edema assessment procedure, and a pulmonary edema assessmentprocedure.

Additionally, time domain signals may be obtained from a patient in anormative or near-normative condition (for example, during rest orduring a time period prior to or after a surgical, therapeutic, ordiagnostic procedure). Such signals, obtained under normative patientconditions, may provide baseline information regarding the patient'sstatus for comparison to a status of the patient in a stressed state.

FIG. 2 depicts a non-limiting example of a computing device that may beincorporated into a physiology monitoring system. Such a computingdevice may incorporate one or more of the following components. It maybe appreciated that additional components not disclosed below, but whichmay be components of computerized systems known in the art, may also beincluded. The number or types of such components may vary, such as morethan one central processing unit or bus. The data connectivity among thedevices also may not be limited to the data connectivity as disclosedbelow. Additional computing, communications, input interfaces, andoutput interfaces beyond those disclosed below may also be consideredincorporated into such a computing device.

A bus 228 may serve as the main information highway interconnecting theother illustrated components of the hardware. A CPU 202 is the centralprocessing unit of the system, performing calculations and logicoperations required to execute a program. Read only memory (ROM) 218 isone example of a static or non-transitory memory device, and randomaccess memory (RAM) 220 is one example of a transitory or dynamic memorydevice.

A controller 204 may interface the system bus 228 with one or moreoptional disk drives 208. These disk drives 208 may include, forexample, external or internal DVD drives, CD ROM drives, or hard drives.

Program instructions, as well as one or more databases, may be stored inthe ROM 218 and/or the RAM 220 or other memory storage device associatedwith the computing device. Optionally, program instructions or one ormore databases may be stored on a computer readable storage medium suchas a compact disk or a digital disk or other recording medium, acommunications signal, or a carrier wave. Additionally, programinstructions, or other data—for example, one or more databases—may bestored on one or more removable memory devices that may include, asnon-limiting examples, one or more removable discs, one or moreremovable cards, one or more removable memory sticks, one or more flashdrives, one or more removable SIM chips, one or more writable CD ROMs orDVD disks, and/or one or more miniature data tapes. Such devices mayalso be used to transfer data from the computing device to another datareceiving device such as a home computer. The computing device may alsoinclude stored records of the patient's status over time in any of itsstorage or memory devices including, without limitation, the ROM 218,RAM 220, disk drives 208, or removable storage media as disclosed above.Such patient-specific data may also be accessible over any datacommunication interface with additional storage devices, such as serversand data-farms. The computing device may also store event trends of thepatient in its storage or memory devices, or store such data in apoint-of-care data facility or in a remotely accessible data repository.

An optional display interface 222 may permit information from the bus228 to be displayed on one or more display devices 224 in audio,graphic, or alphanumeric format. Additional output interface devices mayinclude a monitor a flat-screen display, an LCD panel device, a touchscreen device, an audio device, an LED device, a data communicationslink to a remote output or display device, and a haptic device.Communication with external devices may occur using one or morecommunication ports 226. The one or more communication ports 226 may beconfigured to act as data communication links to additional computingdevices, communication devices, telephony devices, networks, and datarepositories or servers. In some non-limiting examples, the one or moredata communication links may include one or more of an internetconnection, a wireless connection, a telephonic connection, a LANconnection, a WAN connection, and a personal area network. It berecognized that computer instructions, patient medical history data, andone or more databases may be contained in a memory storage deviceaccessible to the computing device over such a data communication link.

Additionally, the computing device may display information at apoint-of-care location, a central care delivery location such as at anurse's station, or at a remote location. Such information may berelated to adverse or emergent events associated with changes inhemodynamic, cardiovascular, and/or volumetric status of the patient. Insome non-limiting examples, such information may include a notificationof emergent events including one or more of an audible signal, a visualsignal on a display, and a text message to a mobile communicationdevice. Visual signals may include, without limitations, indicia on agraphical output on a computer screen (such as arrows to indicatefeatures of importance), texts on a graphical output, and lighteddisplays such as light bulbs, LEDs and other sources of visualinformation that may not be associated with a particular computer screenor monitor.

In addition to the components disclosed above, the hardware may alsoinclude one or more interfaces 212 which may allow for receipt of datafrom one or more input devices 216 such as a keyboard 214, a touchscreen, a mouse, remote control, pointing device, pushbutton, hapticdevice, a voice recognition device, and/or a joystick. An input device216 may also include one or more of a removable memory device and a datacommunication link to a remote device configured to provide input datato the computing device. The one or more interfaces 212 may also receivetime domain physiological signals from the one or more physiologicalsensors via one or more sensor inputs 215.

FIGS. 3A-6B depict several examples of time domain signals acquired bythe computing device, as well as time domain metrics and frequencydomain metrics that may be determined or calculated by the computingdevice.

FIGS. 3A and 3B depict a trace of a pulse volume waveform (time domain)and a power spectrum analysis (frequency domain) of the same waveform,respectively. In some non-limiting examples, the time domain signal maybe transformed into a frequency domain signal by means of a Fouriertransform algorithm. The pulse volume waveforms in FIG. 3A may becharacterized by one or more metrics in the time domain. Examples ofsuch time domain metrics may include peak amplitudes 310 a anddifferences in occurrence times 320 a between successive waveform peaks.It may be understood that the pulse volume waveforms in FIG. 3A maycorrespond to data received in the time domain from a pulse volumesensor such as a photoplethysmograph.

The power spectrum graph in FIG. 3B may be characterized by one or moremetrics in the frequency domain. Non-limiting examples of such frequencydomain metrics may include a fundamental frequency 320 b of the at leastone signal in the time domain, a frequency 330 a,b,c of one or moreinteger harmonics of the at least one signal, a phase value of thefundamental frequency 320 b, a phase value at a frequency 330 a,b,c ofthe one or more integer harmonics of the at least one signal, afrequency change in the fundamental frequency 320 b, a frequency changein one or more frequencies 330 a,b,c of the one or more integerharmonics, a phase change in the fundamental frequency 320 b, a phasechange in one or more frequencies 330 a,b,c of the one or more integerharmonics, a power amplitude 310 b at the fundamental frequency of theat least one signal in the time domain, a power amplitude at eachfrequency of the one or more integer harmonics of the at least onesignal in the time domain, a frequency dispersion about the fundamentalfrequency of the at least one signal, and a frequency dispersion aboutthe frequency of the one or more integer harmonics of the at least onesignal. It may be recognized that frequency dispersion values may bedetermined from the fine structure 340 a associated with the fundamentalfrequency 320 b or the fine structure associated with each of theinteger harmonics (such as the fine structure 345 about the firstinteger harmonic peak 330 a). In some non-limiting examples, thefundamental frequency 320 b may be the fundamental frequencycorresponding to a heart rate.

More complex frequency domain metrics may also be calculated from afrequency domain analysis of the one or more time domain sensingsignals. Thus, in some non-limiting examples, one or more metrics in thefrequency domain may be calculated by the computing device bytransforming the one or more signals in the time domain into one or moresignals in the frequency domain, selecting at least one frequency domainfeature of the one or more signals in the frequency domain, andnormalizing the one or more frequency domain features to one or morefrequency domain feature baseline values.

Some non-limiting examples of frequency domain features that may benormalized to calculate frequency domain metrics may include afundamental frequency 320 b, a frequency 330 a,b,c of one or moreinteger harmonics of the fundamental frequency, a phase value of thefundamental frequency 320 b, a phase value at a frequency 330 a,b,c ofthe one or more integer harmonics of the at least one signal, afrequency change in the fundamental frequency 320 b, a frequency changein one or more frequencies 330 a,b,c of the one or more integerharmonics, a phase change in the fundamental frequency 320 b, a phasechange in one or more frequencies 330 a,b,c of the one or more integerharmonics, a power 310 b at the fundamental frequency, a power at thefrequency 330 a,b,c of the one or more integer harmonics of thefundamental frequency, a change in the fundamental frequency 320 b, achange in the frequency 330 a,b,c of the one or more integer harmonics,a frequency of one or more sidebands of the fundamental frequency, apower at the frequency of the one or more sidebands of the fundamentalfrequency, a dispersion of frequencies about the fundamental frequency,a dispersion of frequencies about the one or more integer harmonics ofthe fundamental frequency, a measure of harmonic distortion, and adispersion of frequencies within one or more sidebands of thefundamental frequency.

It may be understood by one having ordinary skill in the art of signalanalysis that integer harmonics of a fundamental frequency constitutefrequency components at integer multiples of the fundamental frequency.Side-band frequencies, however, may be understood to be frequencycomponents arising from the admixture of two frequencies, f₁ and f₂(wherein f₁>f₂) in which the side-bands appear at frequencies f₁−f₂ andf₁+f₂. Such frequency admixture may occur, for example, due to admixtureof respiratory frequencies with cardiac frequencies.

Some non-limiting examples of frequency domain feature baseline valuesmay include one or more average values of the frequency domain featureof the patient over time, a maximum value of the frequency domainfeature of the patient over time, an average value of the frequencydomain feature from a plurality of patients, a maximum value of thefrequency domain feature from the plurality of patients, an averagevalue of the frequency domain feature of the patient not in a stresscondition, and a maximum value of the frequency domain feature of thepatient not in a stress condition.

In some non-limiting examples, normalizing the one or more frequencydomain features to one or more frequency domain feature baseline valuesmay include dividing, by the computing device, the one or more frequencydomain feature values by the one or more frequency domain featurebaseline values. In another non-limiting example, normalizing the one ormore frequency domain features to at least one frequency domain featurebaseline may include subtracting one or more frequency domain featurebaseline values from the one or more frequency domain feature values toyield one or more numerators, and dividing the one or more numerators bythe one or more frequency domain feature baseline values.

FIG. 4 depicts an example of features that may be obtained from a timedomain pulse wave signal. The pulse wave signal may be characterized byone or more peaks 405 a,b, each peak characterized by a peak amplitude410 a,b (respectively) and a peak occurrence time 415 a,b(respectively). In some embodiments, a time domain metric may becalculated from a plurality of time difference values 420. In otherembodiments, a time domain metric may be calculated from a difference inthe amplitude values 410 a,b of successive peaks 405 a,b (respectively).Additional metrics in the time domain may include one or more of asignal peak amplitude value 410 a,b of the at least one signal in thetime domain, an average of a plurality of signal peak amplitude valuesof the one or more signals in the time domain obtained within aspecified time window, a time difference 420 between an occurrence time415 a of a first signal peak and of a second signal peak 415 b of theone or more signals in the time domain, a dispersion of a plurality ofsignal peak amplitude values of one or more signals in the time domain,and a dispersion of a plurality of time differences between anoccurrence time of a first signal peak and a second signal peak of theone or more signals in the time domain.

More complex time domain metrics may also be calculated from featuresderived from the one or more time domain sensing signals. Thus, in somenon-limiting examples, one or more metrics in the time domain may becalculated by the computing device by selecting at least one time domainfeature of the one or more signals in the time domain, and normalizingthe one or more time domain features to one or more time domain featurebaseline values.

Some non-limiting examples of time domain feature baseline values mayinclude one or more average values of the time domain feature of thepatient over time, a maximum value of the time domain feature of thepatient over time, an average value of the time domain feature from aplurality of patients, a maximum value of the time domain feature fromthe plurality of patients, an average value of the time domain featureof the patient not in a stress condition, and a maximum value of thetime domain feature of the patient not in a stress condition

In some non-limiting examples, normalizing the one or more time domainfeatures to one or more time domain feature baseline values may includedividing, by the computing device, the one or more time domain featurevalues by the one or more time domain feature baseline values. Inanother non-limiting example, normalizing the one or more time domainfeatures to at least one time domain feature baseline may includesubtracting one or more time domain feature baseline values from the oneor more time domain feature values to yield one or more numerators, anddividing the one or more numerators by the one or more time domainfeature baseline values.

Additional metrics may include an analysis of the sensor signalmorphology such as the appearance of multiple peaks or overlapping peaksin a pulse wave signal, or apparent grouping of peaks within one or moretime windows. Characterization of morphology changes may also beperformed in the frequency domain, including, without limitation,changes in phase metrics, or changes in one or more features of thefrequency spectra.

FIGS. 5A and 5B depict dispersion graphs of time differences between theoccurrence times of successive peaks of a pulse wave signal. In someembodiments, such dispersion graphs may take the form of one or morehistograms. A number of time domain metrics may be derived from suchdispersion graphs. FIG. 5A illustrates a dispersion graph of timedifferences between successive pulse waveform peaks for a patientshowing normative (typical or non-pathological) electrocardiac behavior.The dispersion graph in FIG. 5A may be characterized by a narrow primarypeak 505 a centered around a primary time difference 510 a of about 750msec. The primary peak 505 a may represent a normal pulse time(reciprocal pulse rate) corresponding to a pulse rate of about 80 bpm(beats per minute). The primary peak 505 a may be characterized by anynumber of dispersion metrics including, for example, a primary timedifference 510 a and a primary amplitude 515 a. The primary peak 505 amay also be characterized by a primary peak width 520 a. A dispersiongraph peak width metric may be calculated according to any method knownto one skilled in the art including, without limitation, a half-width athalf-maximum (HWHM) or a full-width at half-maximum (FWHM). More complexmetrics for the width of the dispersion graph peak may be derived from afit of the peak to a known curve (such as a Gaussian function) havingknown parameters associated with the curve spread (such as a Guassianfunction a parameter).

FIG. 5B illustrates a dispersion graph of time differences betweensuccessive pulse waveform peaks for a patient having multifocalpremature ventricular beats. The dispersion graph in FIG. 5B may becharacterized by a symmetric primary peak 505 b centered around aprimary time difference 510 b of about 900 msec along with two secondarypeaks 507 a,b centered around secondary time differences 512 a,b ofabout 200 msec and about 1800 msec, respectively. The primary peak 505 bmay be characterized by any number of dispersion metrics including, forexample, a primary time difference 510 b and a primary amplitude 515 b.The primary peak 505 b may also be characterized by a primary peak width520 b. In FIG. 5B, it may be observed that the two secondary peaks 507a,b do not appear to be symmetric based on their respective secondarypeak widths 522 a,b. The two secondary peaks 507 a,b may becharacterized by any number of dispersion metrics including, forexample, secondary time differences 512 a,b (respectively) and secondaryamplitudes 517 a,b (respectively). Although a dispersion graph peakwidth metric associated with the primary peak width 520 b may be readilydescribed by a single value, such as HWHM or FWHM, a more complexdescription of a dispersion graph peak width metric for the twosecondary peaks 517 a,b may be required based on the asymmetry of theirrespective widths 522 a,b.

As disclosed above, additional time domain sensing signals may bereceived by the computing device and used with one or more databases toclassify a patient cardiovascular state, monitor a patient medicalstatus, provide a user with notifications of pre-symptomaticcardiovascular events, and recommend therapies to mitigate theoccurrence of such events. Such additional time domain signals mayinclude, without limitation, time domain signals associated with patientrespiration and time domain signals associated with cardiac electricalpropagation events.

FIGS. 6A and 6B depict electrocardiograph (ECG) traces illustratingfeatures often used by health care providers to assess the nature ofcardiac contractility. Such ECG signals may also constitute time domainphysiological signals received by the monitoring device. The ECG traceis frequently described in terms of the PQRST features, as indicated inFIG. 6A. The P feature generally corresponds to the depolarization ofthe atria of the heart, and is typically initiated at the sinoatrialnode. The QRS complex typically corresponds to ventriculardepolarization and typically is initiated at the atrioventricular node.The P-R time interval generally represents an electrical conduction timelag between the onset of atrial contraction and the onset of ventricularcontraction. The Q-R time interval generally is the total time requiredfor complete ventricular electrical depolarization and hence ventricularcontraction. The T feature corresponds to the repolarization of theventricular tissue, and the S-T interval is a lag time betweenventricular depolarization and the onset of ventricular re-polarization.Other features may be found in an abnormal ECG depending on thepathology. Not shown in FIG. 6A is an R-R interval that generallycorresponds to the time between successive ventricular contractions. Fora normally functioning heart, the R-R interval is associated with theheart rate.

FIG. 6B illustrates an ECG trace characteristic of bradycardia. In FIG.6B, two PQRST features may be observed. Although the PQRST features inFIG. 6B appear superficially the same as depicted in FIG. 6A, the R-Rinterval 610 appears significantly longer than may be found in normativeheart rhythms. Typically, a resting heart rate may be about 50 bpm(beats per minute) to about 60 bpm, providing an R-R interval of about1000 msec to about 1200 msec. It may be understood that athleticallytrained individuals may display unusually long R-R intervals, such asabout 2200 msec. Clinically, however, a waking heart beat below 40 bpm(R-R interval greater than or about 1500 msec) is frequently consideredpathological.

It may be understood that additional metrics in the time domain may bederived from the morphology of one or more ECG traces. Such metrics mayprovide indications of cardiopathologies including, but not limited to,premature ventricular contraction, tachycardia, bradycardia, atrial orventricular fibrillation, re-entrant ventricular stimulation, and AVnode dysrhythmias. As a non-limiting example, FIG. 6C depicts an ECGtrace 630 showing both normal 635 a,b and abnormal 637 ECG waveforms.Morphological ECG metrics in the time domain may be used to distinguishthe normal 635 a,b ECG waveforms from a waveform showing abnormal 637structures, such as those consistent with quadrigeminy. It may also benoted that morphological anomalies found in ECG waveforms may be presentas related anomalies in a pulse waveform. As additionally depicted inFIG. 6C, a pulse waveform 640 obtained at the same time as the ECG trace630 illustrates normal 640 a,b and abnormal 647 waveforms. Morphologicalpulse waveforms metrics in the time domain may also be used to classifyor monitor a patient's cardiovascular state.

As disclosed above, the physiological monitoring system may acquire oneor more time domain sensing signals and derive one or more time domainmetrics and/or one or more frequency domain metrics therefrom. Thesystem may also use information obtained from one or more databases todetermine cardiovascular events either in a pre-symptomatic orsymptomatic patient, or recommend one or more standard therapeuticprotocols or modifications to therapeutic protocols to mitigate suchevents. Non-limiting examples of such databases may include a databaseof cardiovascular states, a database of therapeutic procedures orprotocols, and a database comprising the patient's medical history. Itmay be understood that such databases may take on any format known toone having ordinary skill in the art, including, without limitations,tables, spreadsheets, linked lists, and relational databases. Similarly,one having ordinary skill in the art would understand that the systemmay use, without limitation, one or more of comparative methods,statistical methods, structured query methods, and sorting methods onthe one or more databases in concert with the signals and/or metrics toobtain relevant system outputs for a user.

In some non-limiting embodiments, a database of cardiovascular statesmay contain information obtained from the patient being monitored or aplurality of patients. In some non-limiting embodiments, the informationin the database of cardiovascular states may include one or morecardiovascular states for the patient being monitored or each of theplurality of patients from whom the information has been obtained. Insome other non-limiting embodiments, the information in the database ofcardiovascular states may include one or more metrics in the frequencydomain and/or one or more metrics in the time domain derived from one ormore signals in the time domain from the patient being monitored or fromeach of the plurality of patients from whom the information has beenobtained. Examples of such a time domain signal may include a pulse wavemeasurement from the patient being monitored or from each of theplurality of patients from whom the information has been obtained.Additionally, the information in the database of cardiovascular statesmay include one or more indicators of a medical status of the patientbeing monitored or each of the plurality of patients from whom theinformation has been obtained.

The cardiovascular states included in the one or more databases mayinclude any descriptor of a patient state, including, withoutlimitation, an obstructive sleep disorder state, an anesthesia-inducedhypovolemia state, a hemodialysis-induced hypovolemia state, and ahemodialysis-induced tissue low perfusion state of a patient havingheart failure. It may be recognized that other states may be related topatient responses to pathologies, surgeries, therapeutic procedures, anddiagnostic procedures. In some additional examples, the databases mayinclude sub-groupings of states under more general states. Suchsub-groups, for example, may be used to further classify a patientaccording to an indication of severity of the state or an indication ofthe length of time the patient has been classified as being within thestate.

In addition to a set of cardiovascular states, the one or more databasesmay contain one or more sets of parameters that may be associated witheach of the states. Such parameters may be derived from values of one ormore metrics in the frequency domain and/or time domain.

In one non-limiting example, the one or more sets of parameters mayinclude one or more set of ranges of the one or more metrics, either assigned values or absolute values. Values of time domain and/or frequencydomain metrics obtained from a patient may be compared to such ranges inparameter values. If a metric value is encompassed within a parametervalue range, the metric may be considered “positive” for that parameter.In some instances, cardiovascular states may be subdivided intosub-states encompassing narrower parameter ranges within a largerparameter range of the original state. A patient may thus be classifiedamong such subgroups based on the subrange of values within which thepatient's data falls.

In another non-limiting example, the one or more sets of parameters mayinclude one or more sets of temporal variations (trends or temporalpatterns) of the one or more metrics over some specified time window ofobservation. Temporal variations in values of time domain and/orfrequency domain metrics obtained from a patient may be compared to suchtemporal variation values. If a metric value shows a temporal variationwithin a parameter trend or temporal pattern within the specified timewindow, the metric may be considered “positive” for that parameter. Insome instances, cardiovascular states may be subdivided into sub-statesencompassing degrees of temporal variations. Thus, a sub-state may havean increasing temporal variation having endpoints within the total grouptemporal variation of the original state.

In another non-limiting example, the one or more sets of parameters mayinclude one or more sets of averages or other statistics (such asmeasures of the variation) of the one or more metrics.

In yet another non-limiting example, the one or more sets of parametersmay include one or more sets of threshold values of the one or moremetrics. In one embodiment, if a metric value shows a value greater thana parameter threshold (depending on the metric), the metric may beconsidered “positive” for that parameter. In another embodiment, if ametric value shows a value less than a parameter threshold (depending onthe metric), the metric may be considered “positive” for that parameter.Such threshold values may include average values, maximal values, orother values that can be used to characterize each of the states orsubgroup of states.

It may be understood that the database of cardiovascular states may beupdated at any time. Such updates may include one or more updates to thestates already incorporated in the database, additions of new states tothe database, and subdivisions of states into sub-states. Values forparameters may also be updated, added, or changed as required. In someexamples, an updated database may be loaded into a memory component ofthe physiological monitoring device. In another example, an updateddatabase may be loaded into a remote device, such as a server, in datacommunication with the physiological monitoring device. In still anotherexample, an updated database may be created by the physiologicalmonitoring device by adding monitored patient data including one or moreof the signals in the time domain, one or more metrics in the frequencydomain, and one or more metrics in the time domain derived from one ormore sensing signals obtained from the patient under monitoringconditions. In addition, the physiological monitoring system maycalculate, correlate, or otherwise determine one or more sets ofparameters from the one or more of the signals in the time domain, oneor more metrics in the frequency domain, and one or more metrics in thetime domain derived from one or more sensing signals obtained from thepatient under monitoring conditions. In some non-limiting examples, thecardiovascular state of a patient may be updated during a monitoringsession based on the updated information in the database ofcardiovascular states.

FIG. 7 depicts data that may be included in a database of cardiovascularstates. The left-hand column in FIG. 7 presents potential cardiovascularstates of a patient. The top row in FIG. 7 is a header row of parametersthat may be relevant to classifying a patient among the cardiovascularstates. The entries in FIG. 7 are graphical representations of temporalvariations of the parameters that may be correlated with the classes ofstates. Thus, a single up-arrow (↑) may represent an increase in aparameter value over time and a single down-arrow (↓) may represent adecrease in a parameter value over time. A doubled up-arrow (↑↑) ordown-arrow (↓↓) may represent a sudden or large change in a parametervalue over time (increase and decrease, respectively). In someinstances, a change in a parameter value may not be correlated orassociated with a particular state (denoted by “x”). It may beunderstood that the database of cardiovascular states may include valuesof parameters to characterize the states as disclosed above.

In addition to a database of cardiovascular states, the physiologicalmonitoring device may also contain in a memory component or have accessto one or more of a database of therapeutic protocols and a database ofpatient medical histories. A database of therapeutic protocols mayinclude any of the information in the database of cardiovascular statesalong with one or more descriptors of standardized protocols formitigating cardiovascular pathologies associated with the cardiovascularstates. Such therapeutic protocols may include one or more of apharmaceutical intervention, a surgical intervention, a diuresisintervention, or an electrical intervention. Pharmaceuticalinterventions may include the administration of inotropic drugs orvaso-active drugs according to standardized dosing schedules. Electricalinterventions may include the use of an implantable cardiac pacemaker ormuscular electro-stimulator. A therapeutic database may additionallyinclude parameter settings to be provided to devices used in therapeuticprocedures, for example parameters to be used with a diuresis device tooptimize the removal of fluid from a patient. A therapeutic database mayfurther include ratings of therapeutic protocol effectiveness for eachof the cardiovascular states.

A database of patient medical histories may include any of theinformation in the database of cardiovascular states along with one ormore descriptors related to a patient's medical history. Such indicatorsmay include one or more of an indicator of a patient age, an indicatorof a patient body mass, an indicator of a patient gender, an indicatorof one or more patient co-morbidities, an indicator of one or morepatient medications, an indicator of a dosage of each of the one or morepatient medications, an indicator of one or more patient therapies, anindicator of one or more patient surgeries, and an indicator of one ormore patient genetic predispositions to one or more pathologies.

Additionally, a physiological monitoring system may be used to constructone or more databases including sets of patient cardiovascular statesand parameters derived from sensing signals from patients undermonitoring conditions. Thus, common patient temporal patterns ofresponse may be correlated to static patient data, common interventionstrategies, or parameter settings applied to a machine used in theperformance of the intervention. The physiological monitoring system mayinclude analysis capabilities to statistically correlate temporalpatterns of sensor data, time domain metrics, and frequency domainmetrics to determine parameter ranges, averages, or threshold values toincorporate into the one or more databases. Such a monitoring system mayalso include patient symptom data and medical history data as part ofthe pattern of response in a database repository.

FIG. 8 is a flow chart summarizing an illustrative method of determiningat least a cardiovascular state of a patient using a physiologicalmonitoring device. The monitoring device may receive 810 one or moretime domain signals from a patient including, for example, a pulse wavesignal. The monitoring device may determine 820 at least one metric inthe frequency domain from the time domain signal. The monitoring devicemay receive 825 information from a cardiovascular state database and, inconjunction with the one or more frequency domain metrics, may determine830 a cardiovascular state of the patient.

In addition to the method disclosed above, the monitoring device mayinclude optional capabilities. For example, the monitoring device maynotify 840 a health care provider or other user of possible emergentpatient conditions upon determining 830 the cardiovascular state of thepatient. In another example, the monitoring device may provide 850 ahealth care provider with one or more lists of standardized therapeuticprocedure options. The list may be generated by the device from datareceived from an additional therapeutic database. The list oftherapeutic options may also be ranked according to a metric ofeffectiveness. The monitoring device may also provide 860 a health careprovider or user with a list of alterations or modifications to one ormore standardized therapeutic options based on the patient status asdetermined at the time of patient monitoring. Patient status at the timeof monitoring may include, without limitation, blood pressuremeasurements taken at the time of monitoring, changes in patientmedication (for example, the patient forgot to take required medicationprior to the monitoring session), and patient respiration at the time ofmonitoring. Such a list of alterations to standard therapies may beproduced by the monitoring device in response to receiving 855 patientspecific medical status information in addition to the metrics derivedfrom the one or more sensing signals received 810 from the patient.

The present disclosure is not to be limited in terms of the particularembodiments described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its spirit and scope, as will be apparentto those skilled in the art. Functionally equivalent methods andapparatuses within the scope of the disclosure, in addition to thoseenumerated in this disclosure, will be apparent to those skilled in theart from the foregoing descriptions. Such modifications and variationsare intended to fall within the scope of the appended claims. Thepresent disclosure is to be limited only by the terms of the appendedclaims, along with the full scope of equivalents to which such claimsare entitled. It is to be understood that this disclosure is not limitedto particular methods, reagents, compounds, or compositions, which can,of course, vary. It is also to be understood that the terminology usedin this disclosure is for the purpose of describing particularembodiments only, and is not intended to be limiting.

With respect to the use of substantially any plural and/or singularterms in this disclosure, those having skill in the art can translatefrom the plural to the singular and/or from the singular to the pluralas is appropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth in thisdisclosure for sake of clarity.

It will be understood by those within the art that, in general, termsused in this disclosure, and especially in the appended claims (forexample, bodies of the appended claims) are generally intended as “open”terms (for example, the term “including” should be interpreted as“including but not limited to,” the term “having” should be interpretedas “having at least,” the term “includes” should be interpreted as“includes but is not limited to,” etc.). While various compositions,methods, and devices are described in terms of “comprising” variouscomponents or steps (interpreted as meaning “including, but not limitedto”), the compositions, methods, and devices can also “consistessentially of” or “consist of” the various components and steps, andsuch terminology should be interpreted as defining essentiallyclosed-member groups.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (for example, “a” and/or “an” should be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould be interpreted to mean at least the recited number (for example,the bare recitation of “two recitations,” without other modifiers, meansat least two recitations, or two or more recitations). Furthermore, inthose instances where a convention analogous to “at least one of A, B,and C, etc.” is used, in general such a construction is intended in thesense one having skill in the art would understand the convention (forexample, “a system having at least one of A, B, and C” would include butnot be limited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, etc.). It will be further understood by those within the artthat virtually any disjunctive word and/or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” will be understood to include the possibilities of “A”or “B” or “A and B.”

As will be understood by one skilled in the art, for any and allpurposes, such as in terms of providing a written description, allranges disclosed in this disclosure also encompass any and all possiblesubranges and combinations of subranges thereof. Any listed range can beeasily recognized as sufficiently describing and enabling the same rangebeing broken down into at least equal halves, thirds, quarters, fifths,tenths, etc. As a non-limiting example, each range discussed in thisdisclosure can be readily broken down into a lower third, middle thirdand upper third, etc. As will also be understood by one skilled in theart all language such as “up to,” “at least,” and the like include thenumber recited and refer to ranges which can be subsequently broken downinto subranges as discussed above. Finally, as will be understood by oneskilled in the art, a range includes each individual member.

From the foregoing, it will be appreciated that various embodiments ofthe present disclosure have been described for purposes of illustration,and that various modifications may be made without departing from thescope and spirit of the present disclosure. Accordingly, the variousembodiments disclosed are not intended to be limiting, with the truescope and spirit being indicated by the following claims.

What is claimed is:
 1. A system for determining a cardiovascular stateof a patient in a stress condition, the system comprising: at least onesensor configured to received one or more signals from the patient; acomputing device in data communication with the at least one sensor; anon-transitory, computer-readable storage medium in operablecommunication with the computing device; an input device in operablecommunication with the computing device; and an output device inoperable communication with the computing device; wherein thecomputer-readable storage medium contains one or more programminginstructions that, when executed, cause the computing device to: receiveat least one signal in the time domain from the at least one sensor,wherein the at least one signal in the time domain is a pulse wavemeasurement of the patient; determine at least one metric in the timedomain from the at least one signal in the time domain; determine atleast one metric in the frequency domain from the at least one signal inthe time domain; derive one or more sets of parameters using the atleast one metric in the time domain and the at least one metric in thefrequency domain, a set of parameters being associated with a particularcardiovascular state; evaluate temporal variations of the parameterswithin the one or more sets of parameters; and determine thecardiovascular state of the patient from a combination of the temporalvariations of parameters in a set of parameters and information from atleast one database of cardiovascular states that are associated with thesets of parameters.
 2. The system of claim 1, wherein the input devicecomprises one or more of a keyboard, a mouse, a touch screen, ajoystick, a voice recognition system, a removable memory device, and adata communication link.
 3. The system of claim 1, wherein the outputdevice comprises one or more of a monitor, a flat-screen display, one ormore LED devices, an audio device, and a data communication link.
 4. Thesystem of claim 1, wherein the computer-readable storage medium furthercontains the at least one database.
 5. The system of claim 1, whereinthe at least one database is contained in a memory storage deviceaccessible to the computing device over a data communication link. 6.The system of claim 5, wherein the data communication link comprises oneor more of an internet connection, a wireless connection, a telephonicconnection, a LAN connection, a WAN connection, and a personal areanetwork.
 7. The system of claim 1, wherein the computer-readable storagemedium contains one or more programming instructions that, whenexecuted, further cause the computing device to: transform the at leastone signal in the time domain into at least one signal in the frequencydomain; select at least one frequency domain feature of the at least onesignal in the frequency domain; and normalize the at least one frequencydomain feature to at least one frequency domain feature baseline.
 8. Thesystem of claim 1, wherein the computer-readable storage medium containsone or more programming instructions that, when executed, further causethe computing device to provide a notification to a health care providerof an emergent condition of the combination of the temporal variationsof parameters in a set of parameters and the information from the atleast one database of cardiovascular states.
 9. The system of claim 8,wherein the notification to a health care provider comprises one or moreof an audible signal, a visual signal on a display, and a text messageto a mobile communication device.
 10. The system of claim 1, wherein thecomputer-readable storage medium contains one or more programminginstructions that, when executed, further cause the computing device toprovide a list of one or more therapeutic actions for the patient to ahealth care provider from the combination of the temporal variations ofparameters in a set of parameters and the information from the at leastone database of cardiovascular states.
 11. The system of claim 10,wherein the one or more therapeutic actions comprise one or morestandard therapeutic protocols.
 12. The system of claim 10, wherein theone or more therapeutic actions comprise one or more alterations to oneor more standard therapeutic protocols.
 13. The system of claim 1,wherein the computer-readable storage medium contains one or moreprogramming instructions that, when executed, further cause thecomputing device to: receive from an input device at least one indicatorof a medical status of the patient and provide a list of one or morealterations to one or more standard therapeutic protocols for thepatient to a health care provider from the at least one indicator of themedical status of the patient.
 14. The system of claim 1 wherein thecomputer-readable storage medium contains one or more programminginstructions that, when executed, further cause the computing device to:evaluate temporal variations of the parameters within the one or moresets of parameters by evaluating changes in the parameter value andevaluating large changes in the parameter values; and using the changesand large changes within a set of parameters for determining thecardiovascular state of a patient.
 15. The system of claim 1 wherein thecomputer-readable storage medium contains one or more programminginstructions that, when executed, further cause the computing device to:derive one or more sets of parameters by comparing at least one metricin the frequency domain or at least one metric in the time domain toagainst a threshold value for a parameter.
 16. The system of claim 15wherein the threshold value includes at least one of an average value ora maximum value.
 17. The system of claim 1 wherein the computer-readablestorage medium contains one or more programming instructions that, whenexecuted, further cause the computing device to: derive one or more setsof parameters by comparing at least one metric in the frequency domainor at least one metric in the time domain against a range of values forthe parameter.